Creating high-resolution images from sets of low-resolution images, commonly referred to as super-resolution, is known in the art and many techniques for generating such high-resolution images have been considered. For example, U.S. Pat. No. 5,696,848 to Patti et al. discloses a method of creating a high-resolution image from a sequence of low-resolution images. During the method, a low-resolution image is selected as a reference image to be reconstructed into a high-resolution image. Mapping transformations are produced to map pixels in each low-resolution image into locations of the high-resolution image using motion vectors. The accuracy of motion detection estimation for the motion vectors for each low-resolution image is detected to create an accuracy map. The mapping transformations, accuracy map, aperture time, sensor geometry, optical blur point spread function (PSF) and high-resolution sampling geometry are used to compute a blur PSF. The accuracy map, blur PSF, motion information from the mapping transformations and the low-resolution images are then used to create the high-resolution image.
U.S. Pat. Nos. 5,657,402 and 5,920,657 to Bender et al. disclose a method of creating a high-resolution still image from a plurality of low-resolution image frames. During the method, each image frame is scaled up and the transformation data is mapped into a larger data space representing the high-resolution image. Motion vectors are calculated to allow pixels in one low-resolution image frame to be mapped to the pixels in another low-resolution image frame. Pixels from one low-resolution image frame are then mapped into pixels of the next low-resolution image frame and interpolations are performed to fill spaces between points in the mapped image frames. When larger displacements between image frames occurs, pyramid data structures are used.
U.S. Pat. No. 6,285,804 to Crinon et al. discloses a method of creating a high-resolution image from multiple low-resolution images. During the method, motion vectors that map high-resolution sampling grid points to inter-pixel positions on the low-resolution images are derived. Pixels in the low-resolution images having the shortest distance to each inter-pixel position are identified. One of the identified low-resolution pixels at each high-resolution grid point is selected. Pixel intensity values are mapped to the high-resolution grid points according to the selected low-resolution pixels by interpolation.
Although the above references disclose different techniques for generating high-resolution images from sets of low resolution images, improved methods are desired.
It is therefore an object of the present invention to provide a novel method and system of generating a high-resolution image from a set of low-resolution images.